Boosting Order-Preserving and Transferability for Neural Architecture Search: A Refined Approach

Core Concepts

Improving order-preserving ability and transferability in Neural Architecture Search through Supernet Shifting.

Abstract

The article introduces Supernet Shifting, a refined search strategy combining architecture searching with supernet fine-tuning to enhance order-preserving ability. By focusing on top architectures, local order-preserving is improved. The method allows transferring a pre-trained supernet to new datasets efficiently. Experimental results demonstrate better order-preserving ability and performance gains. The approach reduces time cost and hardware restrictions in NAS applications.

Stats

"Flops can be reduced by 5M and the accuracy increases by 0.3% on ImageNet-1K."
"The Kendall’s tau of the accuracy in supernet and after retrain is only 0.17."

Quotes

"The success of transfer learning and pre-trained models proves that weights can be inherited and only need some small changes for different tasks."
"Our method has better transferability compared to other one-shot NAS methods that have to train a new supernet for a new dataset."

Key Insights Distilled From

Boosting Order-Preserving and Transferability for Neural Architecture Search

by Beichen Zhan... at arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11380.pdf

Deeper Inquiries

How does the introduction of Supernet Shifting impact the efficiency of Neural Architecture Search

The introduction of Supernet Shifting significantly impacts the efficiency of Neural Architecture Search (NAS) by improving both global and local order-preserving abilities. By incorporating Supernet Shifting, the supernet is encouraged to focus on top architectures during evolutionary searching, leading to better discernment and improved local order-preserving ability. This refinement strategy ensures that superior architectures are sampled more frequently, allowing for a more precise comparison between different architectures. As a result, the search quality is enhanced, and optimal architectures can be identified more effectively. Additionally, Supernet Shifting enables the transfer of pre-trained supernet models to new datasets with no loss in performance, further enhancing the efficiency of NAS processes.

What are the potential limitations or challenges associated with relying on evolutionary searching algorithms for biased sampling

Relying on evolutionary searching algorithms for biased sampling in Neural Architecture Search (NAS) may pose certain limitations or challenges. One potential challenge is ensuring that the sampling distribution aligns with the desired bias towards superior architectures accurately throughout the search process. Evolutionary algorithms rely on mutation and crossover operations which can introduce randomness into architecture selection, making it challenging to control or predict how biases will affect sampling decisions over time. Another limitation could be related to computational resources as evolutionary algorithms typically require multiple iterations before converging on optimal solutions, potentially increasing time and resource costs compared to other search strategies.

How might the concept of transferability in NAS impact the future development of automated machine learning systems

The concept of transferability in NAS has significant implications for the future development of automated machine learning systems. By enabling pre-trained supernets to be transferred across different datasets efficiently through methods like fine-tuning during searching stages, transferability enhances adaptability and scalability in NAS applications. This capability reduces time costs associated with retraining supernets from scratch for each new dataset while maintaining high performance levels across diverse tasks. Transferability also promotes knowledge sharing between datasets and tasks, facilitating faster model deployment and optimization in various domains without compromising accuracy or effectiveness.